Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors

Yan, Xinping; Wang, Kai; Yuan, Yupeng; Jiang, Xiaoli; Negenborn, Rudy R.

doi:10.1016/j.oceaneng.2018.08.050

Cited by 75 publications

(26 citation statements)

References 39 publications

(36 reference statements)

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“…Scholars have used big data and AI to achieve better energy efficiency in maritime transport from different perspectives. While the majority of the studies focus on vessel speed optimisation (Lee et al 2018a;Yan et al 2018), others focus on route planning (Liu et al 2018) and vessel crane control (Sanfilippo et al 2017). Brouer, Karsten, and Pisinger (2017) considered a broader scenario and optimised a liner shipping network focusing on container routing and speed optimisation.…”

Section: Energy Efficiencymentioning

confidence: 99%

“…Thus, using big data, Lee et al (2018a) minimised fuel consumption while maximising SLA using the particle swarm optimisation method. Yan et al (2018) found the optimal speed of inland ships using the distributed parallel k-means clustering algorithm. Their proposed approach is effective in reducing vessel energy consumption and CO 2 emissions.…”

Section: Speed Optimisationmentioning

confidence: 99%

“…should be included in the optimisation model for vessel speed optimisation (Lee et al 2018a). Also, future studies should attempt theoretical development for ship energy efficiency optimisation (Yan et al 2018).…”

Section: Future Researchmentioning

confidence: 99%

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Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

Munim

Dushenko

Jimenez

et al. 2020

Maritime Policy & Management

191

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This study provides a bibliometric review of 279 studies on the applications of big data and artificial intelligence (AI) in the maritime industry, published in 214 academic outlets, authored by 842 scholars. We extracted bibliographical data from the Web of Science database and analysed it using the Bibliometrix tool in R software. Based on citation analysis metrics, we revealed the most influential articles, journals, authors and institutions. Using the bibliographic coupling methodology, we identified four underlying research clusters: (1) digital transformation in maritime industry, (2) applications of big data from AIS, (3) energy efficiency and (4) predictive analytics. We analysed these clusters in detail and extracted future research questions. Besides, we present research collaboration networks on the institution and author level.

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Section: Energy Efficiencymentioning

confidence: 99%

Section: Speed Optimisationmentioning

confidence: 99%

See 1 more Smart Citation

Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

Munim

Dushenko

Jimenez

et al. 2020

Maritime Policy & Management

191

View full text Add to dashboard Cite

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“…Outside the scope of weather routing, research work on the problem of speed optimization, taking into account the weather conditions, and defining the problem in similar context as the present work can only be found in very recent publications. 28–32…”

Section: Introductionmentioning

confidence: 99%

“…Results of application of the method to an existing vessel are presented. Big data analytics are applied by Yan et al, 32 in order to model the ship’s performance on different weather conditions. Then, a speed optimization problem for minimizing the consumption of the vessel on a particular route is posed and solved by an appropriate particle swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Synthesis, design and operation optimization of a combined cycle integrated energy system including optimization of the seasonal speed of a VLCC

Sakalis

Tzortzis

Frangopoulos

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The study of an integrated energy system is presented in this article that will cover all types of energy loads (mechanical, electrical, thermal) on a Very Large Crude Carrier (VLCC) with the maximum technically possible and economically feasible exploitation of fuel energy, thus reducing the operating cost and environmental footprint of the ship. There may be a large variety of configurations, design specifications and operating states that can cover the loads, making it necessary to apply synthesis, design and operation optimization of the system. The net present value of the system is selected as the objective function. For this purpose, a superconfiguration of the system is considered, which includes a number of Diesel engines adapted for possible operation in a combined Diesel and Rankine cycle, heat recovery steam generators producing high and low pressure steam, steam turbines that can contribute to propulsion and/or to the electricity production, an exhaust gas boiler and auxiliary boilers, as well as Diesel-generator sets. The synthesis of the system, that is, the components that will finally exist in the system, and their interconnections, the design specification of the components and the operating properties at characteristic operating states of the ship are not predetermined, but they are the result of formal, mathematical optimization. In addition, the speed of the vessel in each state, an important operational variable that has a crucial effect on the propulsion power and thus on the fuel consumption, is also determined by the optimization. The hull characteristics, the loading condition and the weather state are taken into account for the calculation of the propulsion power. For the optimization, proper models of the various components have been developed and the optimization problem is solved by addressing the three levels (synthesis, design, and operation) at a single computational step. The benefits of optimization, as well as the conditions that make the combined cycle economically justified, are demonstrated through an application example. The numerical solution is obtained for various values of fuel price and freight rate, so that the effects of these two crucial parameters on the optimal solution are assessed.

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Building the Remote Surveying System of Energy Consumption in Maritime Transportation Using Internet of Things (IoT) Technique

Tran

2022

Handbook of Smart Energy Systems

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Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors

Cited by 75 publications

References 39 publications

Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

Synthesis, design and operation optimization of a combined cycle integrated energy system including optimization of the seasonal speed of a VLCC

Building the Remote Surveying System of Energy Consumption in Maritime Transportation Using Internet of Things (IoT) Technique

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